MAR 22, 2011 4:48pm ET

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Retroactive Data Quality

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As I, and many others, have blogged about many times before, the proactive approach to data quality, i.e., defect prevention, is highly recommended over the reactive approach to data quality, i.e., data cleansing.

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Comments (1)
Does anyone know where to get a flux capacitor these days? They are in short supply! Good news is you can buy a used Delorean for less than the price of a flux capacitor!

I suggest many organizations have traveled to the past numerous times. It is obvious from the fact that they keep doing the same things over and over again so they can predict the outcomes in advance. I think that gives them some comfort. Of course this may be nothing more than an episode from the movie Groundhog Day.

The problem is current data quality practices cannot "predict" the next error, except on the basis of having seen the error in the past. There are techniques to predict the probability of errors in large data sets that few organizations use or are aware of but these techniques do not identify a single instance error which is what we are seeking!

In some industries such as financial services, some of the underlying data quality problems are systemic. Asset ratings and the ever more complex risk assessments are two such examples. Both of these have been issues for decades and the industry tolerated the sometimes unimaginable variances in the data.

E-mail address accuracy is another challenge. There are ways to authenticate and validate as well as statistical techniques to assess the overall quality of e-mail address, but single instance errors are difficult to prevent.

Data quality is retroactive while we hope it will be a prophylactic!

Posted by Richard O | Wednesday, March 23 2011 at 10:33AM ET
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